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Learning When to Apply Diving Heuristics, Feasibility Pump and Cutting Planes in Mixed-Integer Linear Programming

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Názov: Learning When to Apply Diving Heuristics, Feasibility Pump and Cutting Planes in Mixed-Integer Linear Programming
Autori: Thiago Alcântara Luiz, Samuel Souza Brito, Haroldo Gambini Santos, Marcone Jamilson Freitas Souza, Túlio Ângelo Machado Toffolo
Zdroj: TEM Journal. :1948-1957
Informácie o vydavateľovi: Association for Information Communication Technology Education and Science (UIKTEN), 2025.
Rok vydania: 2025
Popis: Primal heuristics, such as diving heuristics, are fundamental to the performance of modern mixed-integer linear programming (MILP) solvers, playing an essential role in obtaining feasible integer solutions. However, the efficacy of these heuristics depends on the characteristics of the MILP problem solved. To assist solvers in selecting the best heuristics, a recommendation system based on machine learning is proposed. According to the characteristics of the problem, the system recommends which diving heuristic to use and whether it should be combined with feasibility pump and/or cutting planes. To train the model, a dataset was built from 320 optimization problems using 207 features and evaluated using a hybrid diving heuristic approach that enables combining feasibility pump and cutting planes to produce feasible solutions. Computational results show that the recommendation system leads to producing feasible solutions for 87% of the possible cases. This is equivalent to 10% more problem instances than the best diving heuristic combined with feasibility pump and cutting planes, requiring only 52.7% of the runtime.
Druh dokumentu: Article
Jazyk: English
ISSN: 2217-8333
2217-8309
DOI: 10.18421/tem143-04
Rights: CC BY NC ND
Prístupové číslo: edsair.doi...........97f4ae8cf7da7f1f2bd7359752fc8fbb
Databáza: OpenAIRE
Popis
Abstrakt:Primal heuristics, such as diving heuristics, are fundamental to the performance of modern mixed-integer linear programming (MILP) solvers, playing an essential role in obtaining feasible integer solutions. However, the efficacy of these heuristics depends on the characteristics of the MILP problem solved. To assist solvers in selecting the best heuristics, a recommendation system based on machine learning is proposed. According to the characteristics of the problem, the system recommends which diving heuristic to use and whether it should be combined with feasibility pump and/or cutting planes. To train the model, a dataset was built from 320 optimization problems using 207 features and evaluated using a hybrid diving heuristic approach that enables combining feasibility pump and cutting planes to produce feasible solutions. Computational results show that the recommendation system leads to producing feasible solutions for 87% of the possible cases. This is equivalent to 10% more problem instances than the best diving heuristic combined with feasibility pump and cutting planes, requiring only 52.7% of the runtime.
ISSN:22178333
22178309
DOI:10.18421/tem143-04